Safety, Tolerability and Efficacy of Up-Titration of Guideline-Directed Medical Therapies for Acute Heart Failure in Elderly Patients: A Sub-Analysis of the STRONG-HF Randomized Clinical Trial
Bibliographic record
Abstract
AIMS: STRONG-HF examined a high-intensity care (HIC) strategy of rapid up-titration of guideline-directed medical therapy (GDMT) and close follow-up after acute heart failure (AHF) admission. We assess the role of age on efficacy and safety of HIC. METHODS AND RESULTS: Hospitalized AHF patients, not treated with optimal GDMT were randomized to HIC or usual care. The primary endpoint of 180-day death or HF readmission occurred equally in older (>65 years, n = 493, 74 ± 5 years) and younger patients (53 ± 11 years, adjusted hazard ratio [aHR] 1.02, 95% confidence interval [CI] 0.73-1.43, p = 0.89). Older patients received slightly lower GDMT to day 21, but same doses at day 90 and 180. The effect of HIC on the primary endpoint was numerically higher in younger (aHR 0.51, 95% CI 0.32-0.82) than older patients (aHR 0.73, 95% CI 0.46-1.15, adjusted interaction p = 0.30), partially related to COVID-19 deaths. After exclusion of COVID-19 deaths, the effect of HIC was similar in younger (aHR 0.51, 95% CI 0.32-0.82) and older patients (aHR 0.63, 95% CI 0.32-1.02, adjusted interaction p = 0.56), with no treatment-by-age interaction (interaction p = 0.57). HIC induced larger improvements in quality of life to day 90 in younger (EQ-VAS adjusted-mean difference 5.51, 95% CI 3.20-7.82) than in older patients (1.77, 95% CI -0.75 to 4.29, interaction p = 0.032). HIC was associated with similar rates of adverse events in older and younger patients. CONCLUSION: High-intensity care after AHF was safe and resulted in a significant reduction of all-cause death or HF readmission at 180 days across the study age spectrum. Older patients have smaller benefits in terms of quality of life.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".